CLT-concrete composite floor systems are a solution for timber buildings with a long-span floor. It yields a reduction of carbon footprint and even eco-friendly structure at the end of its service life. This study will evaluate the structural performance of notched connectors in the CLT-concrete composite floor, comprised of the serviceability stiffness, maximum load, and behavior at failure. The parameters of the test plan are the loaded edge length, the notch depth, the concrete thickness, and the screw length. Other secondary variables are also assessed, such as different loading sequences, speed of test, and timber moisture content. Experimental results prove that the performance of the connector depends significantly but not linearly on the notch depth and the length of the loaded edge. The connector with a deeper notch and a shorter heel will be stiffer and more robust, but it also tends to have a brittle rupture. The test results also help validate a solution for deconstructable connector systems. A nonlinear finite element model of the connector is built and validated versus the experimental results. It yields reasonably good predictions in terms of resistance and can capture the load-slip relationship.

The artificial neural networks approach is applied to estimate the acoustic performance for airborne and impact sound insulation curves of different lightweight wooden floors. The prediction model is developed based on 252 standardized laboratory measurement curves in one-third octave bands (50–5000 Hz). Physical and geometric characteristics of each floor structure (materials, thickness, density, dimensions, mass and more) are utilized as network parameters. The predictive capability is satisfactory, and the model can estimate airborne sound better than impact sound cases especially in the middle-frequency range (250–1000 Hz), while higher frequency bands often show high errors. The forecast of the weighted airborne sound reduction index $R_w$ was calculated with a maximum error of 2 dB. However, the error increased up to 5 dB in the worse case prediction of the weighted normalized impact sound pressure level $L_{n,w}$. The model showed high variations near the fundamental and critical frequency areas which affect the accuracy. A feature attribution analysis explored the essential parameters on estimation of sound insulation. The thickness of the insulation materials, the density of cross-laminated timber slab and the concrete floating floors and the total density of floor structures seem to affect predictions the most. A comparison between wet and dry floor solution systems indicated the importance of the upper part of floors to estimate airborne and impact sound in low frequencies.

Currently, green roof fire risks are not clearly defined. This is because the problem is still not well understood, which raises concerns. The possibility of plants catching fire, especially during drought periods, is one of the reasons for necessary protection measures. The potential fire hazard for roof decks covered with vegetation has not yet been fully explored. The present study analyzes the performance of green roofs in extreme heat conditions by simulating a heat transfer process through the assembly. The main objective of this study was to determine the conditions and time required for the roof deck to reach a critical temperature. The effects of growing medium layer thickness (between 3 and 10 cm), porosity (0.5 to 0.7), and heating intensity (50, 100, 150, and 200 kW/m²) were examined. It was found that a green roof can protect a wooden roof deck from igniting with only 3 cm of soil coverage when exposed to severe heat fluxes for at least 25 minutes. The dependency of failure time on substrate thickness decreases with increasing heating load. It was also found that substrate porosity has a low impact on time to failure, and only at high heating loads.

A prediction model based on artificial neural networks is adapted to forecast the acoustic performance of airborne sound insulation of various lightweight wooden façade walls. A total of 100 insulation curves were used to develop the prediction model. The data are laboratory measurements of façade walls in one-third-octave bands (50 Hz–5 kHz). For each façade wall, geometric and physical information (material type, dimensions, thicknesses, densities, and more) are used as input parameters. The model shows a satisfactory predictive capability for airborne sound reduction. A higher accuracy is obtained at middle frequencies (250 Hz–1 kHz), while lower and higher frequency ranges often show higher deviations. The weighted airborne sound reduction index ($R_w$) of façades can be estimated with a maximum difference of 3 dB. Sometimes, the model shows high variations within fundamental and critical frequencies that influence the predictive precision. A sensitivity analysis is implemented to investigate the significance of parameters in insulation estimations. The material density (i.e., cross-laminated timber panel, gypsum board), thickness of the insulation materials, thickness and spacing between interior studs and the total density of façades are factors of significant weight on predictions. The results also emphasize the importance of façade thickness and the total density of the clustered exterior layers.

Assessing the fire risk of vegetated roofs includes the determination of their possible contribution to fire. Green roof components such as plants and growing media are organic materials and present a fuel that can catch and support the spread of fire. The flammability characteristics of these components were analyzed and compared to a typical roof covering. Growing media with 15% of organic matter were tested using cone calorimeter apparatus. The fuel load and heat release rate of the growing media were measured in both moist (30%) and dry conditions. It was observed that growing media in a moist condition do not present a fire risk, reaching a maximum heat release rate of 33 kW/m². For dry substrates, a peak heat release rate of 95 kW/m² was recorded in the first minute, which then rapidly decreased to 29 kW/m² in the second minute. Compared to a typical bitumen roof membrane, the green roof showed a better fire performance. The literature data report more severe results for plant behavior, reaching peak heat release rates (HRRs) of 397 kW/m² for dried and 176 kW/m² for a green material. However, a rapid decrease in HRR to much lower values occurs in less than 2 min. The results also show that extensive and intensive types of green roofs present 22% and 95% of the additional fire load density when installed on a modified bitumen membrane, 19.7 and 85.8 MJ/m², respectively.

Mass timber is increasingly being employed in constructing low- and mid-rise buildings. One of the primary reasons for using mass timber structures is their sustainability and ability to reduce environmental consequences in the building sector. One criticism of these structures is their lower subjective sound insulation quality. Therefore, acoustic treatments should be considered. However, acoustic solutions do not necessarily contribute to lower environmental impacts or improved thermal insulation performance. This paper discusses a design methodology that incorporates the development of a sound insulation prediction tool (using an artificial neural networks approach), life cycle assessment analysis, and thermal insulation study. A total of 112 sound insulation measurements (in one-third octave bands from 50 to 5000 Hz) are utilized to develop the network model and are also used for the LCA and thermal insulation study. They are lab-based measurements and are performed on 45 various CLT- and ribbed CLT-based assemblies. The acoustic model demonstrates satisfactory results with 1 dB differences in the prediction of airborne and impact sound indices ($R_w$ and $L_{n,w}$). An acoustic sensitivity study and a statistical analysis are then conducted to validate the model’s results. Additionally, an LCA analysis is performed on the floor assemblies to calculate their environmental footprints. LCA categories are plotted against the acoustic performance of floors. No correlations are found, and the results emphasize that a wide range of sound insulation can be achieved with similar environmental impacts. Within each acoustic performance tier, the LCA results can be optimized for a floor assembly by selecting appropriate materials. The thermal insulation of floors is then calculated. Overall, a strong positive correlation is found between the total thermal resistance and heat loss against acoustic performance. Designers should be cognizant of the trade-offs between acoustic, thermal insulation, and environmental performance when choosing assemblies with favorable environmental impacts relative to acoustic and thermal insulation ratios.